Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …

Abstract Machine learning is a powerful tool that can provide a way to revolutionize the
material science. Its use for the designing and screening of materials for polymer solar cells …

All-small-molecule organic solar cell (ASM OSC) with an efficiency of up to 17.18% is
demonstrated via combining layer-by-layer (LbL) deposition and solid additive methoxy …

Side‐chain tailoring is a promising method to optimize the performance of organic solar cells
(OSCs). However, asymmetric alkyl chain‐based small molecular acceptors (SMAs) are still …

Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …

Solubility plays a critical role in many aspects of research (drugs to materials). Solubility
parameters are very useful for selecting appropriate solvents/non-solvents for various …

Herein, we synthesized new hetero‐halogenated end groups with well‐determined
fluorinated and chlorinated substitutions (o‐FCl‐IC and FClF‐IC), and synthesized …

The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing
continuously, however, commercialization is far from being achieved due to the very high …

Despite the dihalogenation of terminals being an effective strategy to produce efficient
nonfullerene acceptor (NFA)-based organic solar cells (OSCs), hetero-dihalogenated …

Fluorine‐contained polymers, which have been widely used in highly efficient polymer solar
cells (PSCs), are rather costly due to their complicated synthesis and low yields in the …